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Publicação
Concept Drift Awareness in Twitter Streams
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Orientador(es)
Resumo(s)
Learning in non-stationary environments is not an easy task and requires a distinctive approach. The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones. Additionally, given the significant importance that social networks gained as information networks, there is an evergrowing interest in the extraction of complex information used for trend detection, promoting services or market sensing. This
dynamic nature tends to limit the performance of traditional static learning models and dynamic learning strategies must be put forward. In this paper we present a learning strategy to learn with
drift in the occurrence of concepts in Twitter. We propose three different models: a time-window model, an ensemble-based model and an incremental model. Since little is known about the types of drift that can occur in Twitter, we simulate different types of drift by artificially timestamping real Twitter messages in order to evaluate and validate our strategy. Results are so far encouraging
regarding learning in the presence of drift, along with classifying messages in Twitter streams.
Descrição
Palavras-chave
Twitter Adaptation models Time-frequency analysis Event detection Context Vectors
Contexto Educativo
Citação
J. Costa, C. Silva, M. Antunes and B. Ribeiro, "Concept Drift Awareness in Twitter Streams," 2014 13th International Conference on Machine Learning and Applications, Detroit, MI, USA, 2014, pp. 294-299, doi: 10.1109/ICMLA.2014.53
Editora
IEEE
Licença CC
Sem licença CC